{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T11:38:05Z","timestamp":1772192285276,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precipitation is a fundamental component of the Earth\u2019s hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial distribution. Satellite precipitation products (SPPs) are an alternative source of rainfall data. This study aimed to evaluate the performance of PERSIANN-CCS and PDIR-Now SPPs over the Tulij\u00e1 River Basin (Chiapas, Mexico) using scatter plots, categorical statistics, descriptive statistics, and decomposing total bias. Additionally, bias correction was performed using the quantile mapping (QM) method. QM is a technique used to improve the fit of SPPs with respect to rainfall observations through a transfer function, aiming to reduce systematic errors in SPPs. The results indicate that the PDIR-Now product tends to overestimate rainfall to a large extent, thus showing better performance in detecting rain events. Meanwhile, PERSIANN-CCS underestimates precipitation to a lesser extent. The findings of this study demonstrate that correcting the bias of SPPs improves estimations of rainfall records, thereby reducing the percentage bias and root mean square error.<\/jats:p>","DOI":"10.3390\/rs16142596","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T15:05:51Z","timestamp":1721142351000},"page":"2596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Assessment of PERSIANN Satellite Products over the Tulij\u00e1 River Basin, Mexico"],"prefix":"10.3390","volume":"16","author":[{"given":"Lorenza","family":"Ceferino-Hern\u00e1ndez","sequence":"first","affiliation":[{"name":"Instituto Interamericano de Tecnolog\u00eda y Ciencias del Agua, Universidad Aut\u00f3noma del Estado de M\u00e9xico, km 14.5 Carretera Toluca-Ixtlahuaca, Estado de M\u00e9xico 50200, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0684-4282","authenticated-orcid":false,"given":"Francisco","family":"Maga\u00f1a-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n Acad\u00e9mica de Ingenier\u00eda y Arquitectura (DAIA), Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco, Carretera Cunduac\u00e1n-Jalpa de M\u00e9ndez km. 1, Cunduac\u00e1n 86690, Mexico"}]},{"given":"Enrique","family":"Campos-Campos","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n Acad\u00e9mica de Ingenier\u00eda y Arquitectura (DAIA), Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco, Carretera Cunduac\u00e1n-Jalpa de M\u00e9ndez km. 1, Cunduac\u00e1n 86690, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1963-2553","authenticated-orcid":false,"given":"Gabriela Adina","family":"Morosanu","sequence":"additional","affiliation":[{"name":"Institute of Geography of the Romanian Academy, 12 Dimitrie Racovi\u021b\u0103, Sector 6, 032993 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6187-4519","authenticated-orcid":false,"given":"Carlos E.","family":"Torres-Aguilar","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n Acad\u00e9mica de Ingenier\u00eda y Arquitectura (DAIA), Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco, Carretera Cunduac\u00e1n-Jalpa de M\u00e9ndez km. 1, Cunduac\u00e1n 86690, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9265-7621","authenticated-orcid":false,"given":"Ren\u00e9 Sebasti\u00e1n","family":"Mora-Ortiz","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n Acad\u00e9mica de Ingenier\u00eda y Arquitectura (DAIA), Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco, Carretera Cunduac\u00e1n-Jalpa de M\u00e9ndez km. 1, Cunduac\u00e1n 86690, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3736-9154","authenticated-orcid":false,"given":"Sergio A.","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n Acad\u00e9mica de Ingenier\u00eda y Arquitectura (DAIA), Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco, Carretera Cunduac\u00e1n-Jalpa de M\u00e9ndez km. 1, Cunduac\u00e1n 86690, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.30564\/jasr.v2i4.1564","article-title":"Spatio-temporal change of atmospheric precipitation on territory of north-west of Ukraine","volume":"2","author":"Budnik","year":"2020","journal-title":"J. Atmos. Sci. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1002\/met.284","article-title":"Global precipitation measurement","volume":"18","author":"Kidd","year":"2011","journal-title":"Meteorol. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2258","DOI":"10.1002\/hyp.10366","article-title":"An improved bias correction scheme based on comparative precipitation characteristics","volume":"29","author":"Kim","year":"2015","journal-title":"Hydrol. Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"074058","DOI":"10.1088\/1748-9326\/ac0fa6","article-title":"Tropical rainfall monitoring with commercial microwave links in Sri Lanka","volume":"16","author":"Overeem","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gilewski, P., and Nawalany, M. (2018). Inter-comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) precipitation estimates performance for rainfall-runoff modeling in a mountainous catchment in Poland. Water, 10.","DOI":"10.3390\/w10111665"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.jhydrol.2012.05.055","article-title":"Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method","volume":"452\u2013453","author":"Jiang","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-13-00068.1","article-title":"PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies","volume":"96","author":"Ashouri","year":"2015","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gavil\u00e1n, V., Lillo-Saavedra, M., Holzapfel, E., Rivera, D., and Garc\u00eda-Pedrero, A. (2019). Seasonal crop water balance using harmonized Landsat-8 and Sentinel-2 time series data. Water, 11.","DOI":"10.3390\/w11112236"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.inffus.2020.01.003","article-title":"Pixel level fusion techniques for SAR and optical images: A review","volume":"59","author":"Kulkarni","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.atmosres.2011.10.021","article-title":"Global precipitation measurement: Methods, datasets and applications","volume":"104\u2013105","author":"Tapiador","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1017\/S1350482704001173","article-title":"Neural networks in satellite rainfall estimation","volume":"11","author":"Tapiador","year":"2004","journal-title":"Meteorol. Appl."},{"key":"ref_12","unstructured":"Ceccato, P., and Dinku, T. (2010). Introduction to Remote Sensing for Monitoring Rainfall, Temperature, Vegetation and Water Bodies, International Research Institute for Climate and Society. IRI Technical Report 10-04."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.5194\/hess-15-1109-2011","article-title":"Status of satellite precipitation retrievals","volume":"15","author":"Kidd","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1175\/1520-0442(2002)015<0983:DVOTRR>2.0.CO;2","article-title":"Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information","volume":"15","author":"Sorooshian","year":"2002","journal-title":"J. Clim."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1080\/014311697217800","article-title":"Objectively determined 10-day African rainfall estimates created for famine early warning systems","volume":"18","author":"Herman","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1175\/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2","article-title":"The Tropical Rainfall Measuring Mission (TRMM) sensor package","volume":"15","author":"Kummerow","year":"1998","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1175\/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2","article-title":"CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution","volume":"5","author":"Joyce","year":"2004","journal-title":"J. Hydrometeorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1175\/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2","article-title":"Evaluation of PERSIANN system satellite-based estimates of tropical rainfall","volume":"81","author":"Sorooshian","year":"2000","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"589","DOI":"10.5194\/hess-21-589-2017","article-title":"MSWEP: 3-hourly 0.25\u00b0 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data","volume":"21","author":"Beck","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_20","first-page":"4","article-title":"A Quasi-Global Precipitation Time Series for Drought Monitoring","volume":"832","author":"Funk","year":"2014","journal-title":"US Geol. Surv. Data Ser."},{"key":"ref_21","first-page":"3414","article-title":"The Global Satellite Mapping of Precipitation (GSMaP) project","volume":"5","author":"Okamoto","year":"2005","journal-title":"Int. Geosci. Remote Sens. Symp. (IGARSS)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s00704-013-0860-x","article-title":"GPCC\u2019s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle","volume":"115","author":"Schneider","year":"2014","journal-title":"Theor. Appl. Climatol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huffman, G.J., Adler, R.F., Bolvin, D.T., and Nelkin, E.J. (2010). The TRMM Multi-satellite Precipitation Analysis (TMPA). Satellite Rainfall Applications for Surface Hydrology, Springer.","DOI":"10.1007\/978-90-481-2915-7_1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1175\/BAMS-D-13-00164.1","article-title":"The global precipitation measurement mission","volume":"95","author":"Hou","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_25","unstructured":"Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E.J., and Xie, P. (2024, February 05). NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version 4.5, Available online: https:\/\/pmm.nasa.gov\/sites\/default\/files\/document_files\/IMERG_ATBD_V4.5.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.atmosres.2018.06.023","article-title":"Validation of CHIRPS precipitation dataset along the Central Andes of Argentina","volume":"213","author":"Rivera","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1002\/qj.3342","article-title":"Evaluation of CHIRPS rainfall estimates over Iran","volume":"144","author":"Saeidizand","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Erazo, B., Bourrel, L., Frappart, F., Chimborazo, O., Labat, D., Dominguez-Granda, L., Matamoros, D., and Mejia, R. (2018). Validation of satellite estimates (Tropical Rainfall Measuring Mission, TRMM) for rainfall variability over the Pacific slope and Coast of Ecuador. Water, 10.","DOI":"10.3390\/w10020213"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.pce.2018.02.010","article-title":"Validation of satellite-based rainfall in Kalahari","volume":"105","author":"Lekula","year":"2018","journal-title":"Phys. Chem. Earth"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1002\/qj.3244","article-title":"Validation of the CHIRPS satellite rainfall estimates over eastern Africa","volume":"144","author":"Dinku","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Katiraie-Boroujerdy, P.S., Naeini, M.R., Asanjan, A.A., Chavoshian, A., Hsu, K.L., and Sorooshian, S. (2020). Bias correction of satellite-based precipitation estimations using quantile mapping approach in different climate regions of Iran. Remote Sens., 12.","DOI":"10.3390\/rs12132102"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3354\/cr023233","article-title":"Assessing future discharge of the river Rhine using regional climate model integrations and a hydrological model","volume":"23","author":"Shabalova","year":"2003","journal-title":"Clim. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.5194\/hess-11-1145-2007","article-title":"Estimates of future discharges of the river Rhine using two scenario methodologies: Direct versus delta approach","volume":"11","author":"Lenderink","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jhydrol.2012.05.052","article-title":"Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods","volume":"456\u2013457","author":"Teutschbein","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10584-011-0224-4","article-title":"Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal","volume":"112","author":"Gobiet","year":"2012","journal-title":"Clim. Chang."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s00704-022-04035-2","article-title":"Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method: Advantages of a linear correction for extremes","volume":"149","author":"Holthuijzen","year":"2022","journal-title":"Theor. Appl. Climatol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.jhydrol.2010.10.024","article-title":"Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models","volume":"395","author":"Piani","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1002\/joc.1287","article-title":"Downscaling from GCM precipitation: A benchmark for dynamical and statistical downscaling methods","volume":"26","author":"Schmidli","year":"2006","journal-title":"Int. J. Climatol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.5194\/hess-16-3383-2012","article-title":"Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations\u2014A comparison of methods","volume":"16","author":"Gudmundsson","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1002\/jgrd.50323","article-title":"Multisegment statistical bias correction of daily GCM precipitation output","volume":"118","author":"Grillakis","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1016\/j.jhydrol.2017.01.058","article-title":"Performance of bias corrected MPEG rainfall estimate for rainfall-runoff simulation in the upper Blue Nile Basin, Ethiopia","volume":"556","author":"Worqlul","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"525","DOI":"10.2166\/wcc.2017.127","article-title":"Nonparametric quantile mapping using the response surface method\u2014Bias correction of daily precipitation","volume":"9","author":"Bong","year":"2018","journal-title":"J. Water Clim. Chang."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3160","DOI":"10.1002\/joc.6008","article-title":"Methodological application of quantile mapping to generate precipitation data over Northwest Himalaya","volume":"39","author":"Devi","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Heo, J.H., Ahn, H., Shin, J.Y., Kjeldsen, T.R., and Jeong, C. (2019). Probability distributions for a quantile mapping technique for a bias correction of precipitation data: A case study to precipitation data under climate change. Water, 11.","DOI":"10.3390\/w11071475"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Serrat-Capdevila, A., Merino, M., Valdes, J.B., and Durcik, M. (2016). Evaluation of the performance of three satellite precipitation products over Africa. Remote Sens., 8.","DOI":"10.3390\/rs8100836"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shukla, A.K., Ojha, C.S.P., Singh, R.P., Pal, L., and Fu, D. (2019). Evaluation of TRMM precipitation dataset over Himalayan Catchment: The upper Ganga Basin, India. Water, 11.","DOI":"10.3390\/w11030613"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ringard, J., Seyler, F., and Linguet, L. (2017). A quantile mapping bias correction method based on hydroclimatic classification of the Guiana shield. Sensors, 17.","DOI":"10.3390\/s17061413"},{"key":"ref_48","unstructured":"Chow, V., Maidment, D., and Mays, L. (2024, February 26). Hidrolog\u00eda Aplicada. Hidrologia Aplicada. Available online: http:\/\/bases.bireme.br\/cgi-bin\/wxislind.exe\/iah\/online\/?IsisScript=iah\/iah.xis&src=google&base=REPIDISCA&lang=p&nextAction=lnk&exprSearch=158911&indexSearch=ID."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5154\/r.inagbi.2015.11.008","article-title":"Estimation of missing daily precipitation and maximum and minimum temperature records in San Luis Potos\u00ed","volume":"8","year":"2016","journal-title":"Ing. Agr\u00edcola Biosist."},{"key":"ref_50","first-page":"577","article-title":"Relleno de series diarias de precipitaci\u00f3n, temperatura m\u00ednima, m\u00e1xima de la regi\u00f3n norte del Urab\u00e1 Antioque\u00f1o","volume":"6","year":"2015","journal-title":"Rev. Mex. Cienc. Agr\u00edcolas"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1175\/JHM574.1","article-title":"Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network","volume":"8","author":"Hong","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1175\/JHM-D-20-0177.1","article-title":"Persiann dynamic infrared\u2013rain rate (PDIR-now): A near-real-time, quasi-global satellite precipitation dataset","volume":"21","author":"Nguyen","year":"2020","journal-title":"J. Hydrometeorol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1038\/s41597-021-00940-9","article-title":"PERSIANN-CCS-CDR, a 3-hourly 0.04\u00b0 global precipitation climate data record for heavy precipitation studies","volume":"8","author":"Sadeghi","year":"2021","journal-title":"Sci. Data"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.5194\/hess-23-1505-2019","article-title":"Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model","volume":"23","author":"Li","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_55","unstructured":"Wilks, D.S. (2007). Statistical methods in the atmospheric sciences, second edition. Meteorological Applications, Academic Press."},{"key":"ref_56","first-page":"166","article-title":"Finley\u2019s Tornado Predictions","volume":"1","author":"Gilbert","year":"1884","journal-title":"Am. Meteorol. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1175\/2007JHM925.1","article-title":"Investigating error metrics for satellite rainfall data at hydrologically relevant scales","volume":"9","author":"Hossain","year":"2008","journal-title":"J. Hydrometeorol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1175\/1520-0434(1990)005<0576:OSMOSI>2.0.CO;2","article-title":"On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables","volume":"5","author":"Doswell","year":"1990","journal-title":"Weather Forecast."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s40710-020-00465-0","article-title":"Evaluation and Comparison of Satellite Rainfall Products in the Black Volta Basin","volume":"8","author":"Logah","year":"2021","journal-title":"Environ. Process."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/02723646.1981.10642213","article-title":"On the validation of models","volume":"2","author":"Willmott","year":"1981","journal-title":"Phys. Geogr."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.atmosres.2009.06.015","article-title":"Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA","volume":"94","author":"Habib","year":"2009","journal-title":"Atmos. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"D24101","DOI":"10.1029\/2009JD011949","article-title":"Component analysis of errors in Satellite-based precipitation estimates","volume":"114","author":"Tian","year":"2009","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"151679","DOI":"10.1016\/j.scitotenv.2021.151679","article-title":"Bias correction framework for satellite precipitation products using a rain\/no rain discriminative model","volume":"818","author":"Xiao","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Adane, G.B., Hirpa, B.A., Lim, C.H., and Lee, W.K. (2021). Evaluation and comparison of satellite-derived estimates of rainfall in the diverse climate and terrain of central and northeastern ethiopia. Remote Sens., 13.","DOI":"10.3390\/rs13071275"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Belabid, N., Zhao, F., Brocca, L., Huang, Y., and Tan, Y. (2019). Near-real-time flood forecasting based on satellite precipitation products. Remote Sens., 11.","DOI":"10.3390\/rs11030252"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Cheng, S., Wang, W., and Yu, Z. (2021). Evaluating the drought-monitoring utility of GPM and TRMM precipitation products over mainland china. Remote Sens., 13.","DOI":"10.3390\/rs13204153"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chua, Z.W., Kuleshov, Y., Watkins, A.B., Choy, S., and Sun, C. (2022). A Two-Step Approach to Blending GSMaP Satellite Rainfall Estimates with Gauge Observations over Australia. Remote Sens., 14.","DOI":"10.3390\/rs14081903"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Palharini, R.S.A., Vila, D.A., Rodrigues, D.T., Quispe, D.P., Palharini, R.C., de Siqueira, R.A., and de Sousa Afonso, J.M. (2020). Assessment of the extreme precipitation by satellite estimates over South America. Remote Sens., 12.","DOI":"10.3390\/rs12132085"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"C\u00e1novas-Garc\u00eda, F., Garc\u00eda-Galiano, S., and Alonso-Sarr\u00eda, F. (2018). Assessment of satellite and radar quantitative precipitation estimates for real time monitoring of meteorological extremes over the southeast of the Iberian Peninsula. Remote Sens., 10.","DOI":"10.20944\/preprints201805.0150.v1"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"101360","DOI":"10.1016\/j.ejrh.2023.101360","article-title":"A comprehensive evaluation of the satellite precipitation products across Iran","volume":"46","author":"Dehaghani","year":"2023","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"5801","DOI":"10.5194\/hess-22-5801-2018","article-title":"The PERSIANN family of global satellite precipitation data: A review and evaluation of products","volume":"22","author":"Nguyen","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Huang, W.R., Liu, P.Y., Hsu, J., Li, X., and Deng, L. (2021). Assessment of near-real-time satellite precipitation products from gsmap in monitoring rainfall variations over Taiwan. Remote Sens., 13.","DOI":"10.3390\/rs13020202"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"101109","DOI":"10.1016\/j.ejrh.2022.101109","article-title":"Hydrological application and accuracy evaluation of PERSIANN satellite-based precipitation estimates over a humid continental climate catchment","volume":"41","author":"Eini","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Hsu, J., Huang, W.R., and Liu, P.Y. (2022). Comprehensive Analysis of PERSIANN Products in Studying the Precipitation Variations over Luzon. Remote Sens., 14.","DOI":"10.3390\/rs14225900"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"478","DOI":"10.3934\/geosci.2021027","article-title":"Artificial neural network based PERSIANN data sets in evaluation of hydrologic utility of precipitation estimations in a tropical watershed of Sri Lanka","volume":"7","author":"Gunathilake","year":"2021","journal-title":"AIMS Geosci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2596\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:17:36Z","timestamp":1760109456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2596"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,16]]},"references-count":75,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16142596"],"URL":"https:\/\/doi.org\/10.3390\/rs16142596","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,16]]}}}